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facebook-github-bot merged 1 commit into from
Aug 23, 2025
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migrate all test_aten_ops to facto #13483

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155 changes: 138 additions & 17 deletions backends/cadence/utils/facto_util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,8 @@
from functools import lru_cache
from typing import List, OrderedDict, Tuple

import facto.specdb.function as fn

import torch
from facto.inputgen.argtuple.gen import ArgumentTupleGenerator
from facto.inputgen.specs.model import ConstraintProducer as cp
Expand All @@ -22,21 +24,28 @@

def apply_tensor_contraints(op_name: str, index: int) -> list[object]:
tensor_constraints = [
cp.Dtype.In(lambda deps: [torch.int, torch.float]),
cp.Dtype.NotIn(lambda deps: [torch.int64, torch.float64]),
cp.Dtype.In(
lambda deps: [
torch.int8,
torch.int16,
torch.uint8,
torch.uint16,
torch.float32,
]
),
cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
cp.Value.Le(lambda deps, dtype, struct: 2**4),
cp.Rank.Ge(lambda deps: 1),
cp.Size.Ge(lambda deps, r, d: 1),
cp.Size.Le(lambda deps, r, d: 2**9),
cp.Rank.Le(lambda deps: 2**3),
]

match op_name:
case "where.self":
if index == 0: # condition
tensor_constraints = [
cp.Dtype.In(lambda deps: [torch.bool]),
cp.Dtype.NotIn(lambda deps: [torch.int64, torch.float64]),
cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
cp.Value.Le(lambda deps, dtype, struct: 2**4),
cp.Rank.Ge(lambda deps: 1),
Expand All @@ -45,28 +54,43 @@ def apply_tensor_contraints(op_name: str, index: int) -> list[object]:
]
else:
tensor_constraints = [
cp.Dtype.In(lambda deps: [torch.float, torch.int]),
cp.Dtype.NotIn(lambda deps: [torch.int64, torch.float64]),
cp.Dtype.In(
lambda deps: [
torch.int8,
torch.int16,
torch.uint8,
torch.uint16,
torch.float32,
]
),
cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
cp.Value.Le(lambda deps, dtype, struct: 2**4),
cp.Rank.Ge(lambda deps: 1),
cp.Size.Ge(lambda deps, r, d: 1),
cp.Size.Le(lambda deps, r, d: 2**9),
]
case "embedding.default":
tensor_constraints = [
cp.Dtype.In(lambda deps: [torch.float, torch.int]),
cp.Dtype.NotIn(lambda deps: [torch.int64, torch.float64]),
cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
cp.Value.Le(lambda deps, dtype, struct: 2**4),
cp.Rank.Ge(lambda deps: 1),
cp.Size.Ge(lambda deps, r, d: 1),
cp.Size.Le(lambda deps, r, d: 2**9),
]
case "sigmoid.default":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float]),
cp.Rank.Le(lambda deps: 2**2),
cp.Dtype.In(lambda deps: [torch.float32]),
cp.Value.Ge(lambda deps, dtype, struct: -2),
cp.Value.Le(lambda deps, dtype, struct: 2),
]
)
case "rsqrt.default":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float]),
cp.Rank.Le(lambda deps: 2**2),
cp.Dtype.In(lambda deps: [torch.float32]),
cp.Value.Gt(
lambda deps, dtype, struct: 0
), # only generate real numbers
Expand All @@ -76,14 +100,12 @@ def apply_tensor_contraints(op_name: str, index: int) -> list[object]:
case "mean.dim":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float]),
cp.Rank.Le(lambda deps: 2**2),
cp.Dtype.In(lambda deps: [torch.float32]),
]
)
case "exp.default":
tensor_constraints.extend(
[
cp.Rank.Le(lambda deps: 2**3),
cp.Value.Ge(lambda deps, dtype, struct: -(2**2)),
cp.Value.Le(lambda deps, dtype, struct: 2**2),
]
Expand All @@ -96,26 +118,96 @@ def apply_tensor_contraints(op_name: str, index: int) -> list[object]:
cp.Value.Le(lambda deps, dtype, struct: 2),
]
)
case _:
case "constant_pad_nd.default":
tensor_constraints.extend(
[
cp.Rank.Le(lambda deps: 2**2),
cp.Dtype.In(lambda deps: [torch.float32]),
cp.Size.Le(lambda deps, r, d: 2**2),
]
)
case "avg_pool2d.default":
tensor_constraints.extend(
[
cp.Rank.Eq(lambda deps: 4),
]
)
case "bmm.default" | "addmm.default" | "mm.default":
tensor_constraints.extend(
[
cp.Dtype.Eq(lambda deps: torch.float),
cp.Size.Le(lambda deps, r, d: 2**2),
cp.Value.Le(lambda deps, dtype, struct: 2**4),
]
)
case "div.Tensor":
tensor_constraints.extend(
[
cp.Value.Ne(lambda deps, dtype, struct: 0),
]
)
case "div.Tensor_mode" | "minimum.default":
if index == 0:
tensor_constraints = [
cp.Dtype.In(lambda deps: [torch.int64, torch.int32, torch.float32]),
cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
cp.Value.Le(lambda deps, dtype, struct: 2**4),
cp.Rank.Ge(lambda deps: 1),
cp.Size.Ge(lambda deps, r, d: 1),
cp.Size.Le(lambda deps, r, d: 2**2),
]
else:
tensor_constraints = [
cp.Dtype.In(lambda deps: [torch.int64, torch.int32, torch.float32]),
cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
cp.Value.Le(lambda deps, dtype, struct: 2**4),
cp.Rank.Ge(lambda deps: 1),
cp.Rank.Eq(lambda deps: deps[0].dim()),
cp.Size.Eq(lambda deps, r, d: fn.safe_size(deps[0], d)),
]
case "_native_batch_norm_legit_no_training.default":
tensor_constraints.extend(
[
cp.Rank.Le(lambda deps: 3),
],
)
case "reciprocal.default":
tensor_constraints = [
cp.Value.Ge(lambda deps, dtype, struct: -(2**2)),
cp.Value.Le(lambda deps, dtype, struct: 2**2),
cp.Size.Le(lambda deps, r, d: 2**3),
]
case "_softmax.default":
tensor_constraints.extend(
[
cp.Dtype.Eq(lambda deps: torch.float32),
cp.Size.Le(lambda deps, r, d: 2**2),
]
)
case _:
pass
return tensor_constraints


def apply_scalar_contraints(op_name: str) -> list[ScalarDtype]:
match op_name:
case "add.Scalar" | "sub.Scalar" | "mul.Scalar" | "div.Scalar":
case (
"add.Scalar"
| "sub.Scalar"
| "mul.Scalar"
| "div.Scalar"
| "constant_pad_nd.default"
):
return [ScalarDtype.int]
case "full.default":
return [ScalarDtype.int]

case _:
return [ScalarDtype.float, ScalarDtype.int]


@lru_cache(maxsize=None)
def facto_testcase_gen(op_name: str) -> List[Tuple[List[str], OrderedDict[str, str]]]:
def facto_testcase_gen( # noqa: C901
op_name: str,
) -> List[Tuple[List[str], OrderedDict[str, str]]]:
# minimal example to test add.Tensor using FACTO
spec = SpecDictDB[op_name]

Expand Down Expand Up @@ -149,6 +241,12 @@ def facto_testcase_gen(op_name: str) -> List[Tuple[List[str], OrderedDict[str, s
cp.Dtype.In(lambda deps: apply_scalar_contraints(op_name)),
]
)
if in_spec.name == "dtype": # full.default
spec.inspec[index].constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.long, torch.float]),
]
)
elif in_spec.type.is_tensor():
spec.inspec[index].constraints.extend(
apply_tensor_contraints(op_name, index)
Expand All @@ -166,6 +264,29 @@ def facto_testcase_gen(op_name: str) -> List[Tuple[List[str], OrderedDict[str, s
cp.Dtype.In(lambda deps: [torch.bool]),
]
)
elif in_spec.type.is_length_list():
spec.inspec[index].constraints.extend(
[
cp.Value.Ge(lambda deps, dtype, struct: 0),
]
)
if op_name == "avg_pool2d.default":
spec.inspec[index].constraints.extend(
[
cp.Length.Eq(lambda deps: 2),
]
)
elif in_spec.type.is_shape():
spec.inspec[index].constraints.extend(
[
cp.Rank.Ge(lambda deps: 1),
cp.Rank.Le(lambda deps: 2**2),
cp.Value.Gt(lambda deps, dtype, struct: 0),
cp.Value.Le(lambda deps, dtype, struct: 2**2),
cp.Size.Ge(lambda deps, r, d: 1),
cp.Size.Le(lambda deps, r, d: 2**2),
]
)

return [
(posargs, inkwargs)
Expand Down
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